Amazon

Manager of Applied Science for Amazon Ads Measurement Science team, focusing on developing and leading a team to create solutions for performance measurement products. This includes leveraging Generative AI, classical ML, Causal Inference, NLP, and Computer Vision to measure ad spend impact, provide insights, and potentially develop agentic AI solutions for advertisers. The role involves managing scientists, defining strategy, and delivering scientific solutions.

Manager of Applied Science for Amazon Ads Measurement Science team, focusing on developing and leading a team to create solutions for performance measurement products. This includes leveraging Generative AI, classical ML, Causal Inference, NLP, and Computer Vision to measure ad spend impact, provide insights, and potentially develop agentic AI solutions for advertisers. The role involves managing scientists, defining strategy, and delivering scientific solutions.

Senior Applied Scientist role focused on developing and productionizing media planning models and solutions for Amazon Ads. The role involves leveraging various ML techniques, including deep learning, generative AI, causal inference, NLP, and computer vision, to provide measurement insights and optimize advertising outcomes. It requires collaboration with engineering, product management, and business teams, and includes mentoring junior scientists and engaging with the scientific community.

This role focuses on developing and implementing innovative media planning and measurement solutions within Amazon Ads. It involves creating end-to-end ML/DL models, from inception to production, to provide insights and recommendations for advertisers. The role requires a strong foundation in ML/DL, experience with productionizing models, and the ability to work with large datasets and collaborate with cross-functional teams.

Internship role focused on building AI infrastructure and ML systems, including custom silicon design, framework optimization, compiler development, and distributed training systems for AWS.

The role is for a Senior AI Innovation & GTM Programs Lead within AWS Applied AI Solutions. The primary focus is on driving organizational effectiveness by building AI-powered tools and automation using technologies like Amazon QuickSight and Kiro to streamline Go-To-Market (GTM) processes, enhance account planning, and accelerate business growth. The role involves owning the technical roadmap for AI innovation, prototyping new capabilities, and translating business needs into technical solutions.

Software Development Engineer II role focused on building and deploying machine learning models and systems for profit intelligence within Amazon. Responsibilities include production deployment, architecture design, automation of ML pipelines, and metrics design for model effectiveness. The role leverages AWS cloud services and distributed systems.

Software Development Engineer I (SDE I) role focused on building Gen AI solutions for freight logistics, including carrier-facing features and internal tools to boost developer productivity. The role involves end-to-end software delivery, leveraging technologies like Agentcore and Bedrock, and optimizing pricing algorithms within Amazon's middle-mile network.

Senior Applied Scientist role focused on applying AI/ML and GenAI to credit management, B2B financial services, and payments within Amazon. The role involves developing and deploying production models for credit and fraud management, enhancing product features, and leveraging alternative data. It requires experience with foundation models, agentic frameworks, and LLM-powered automation, with a strong emphasis on operational excellence and delivering customer value through AI-driven insights and automation.

This role focuses on building and deploying ML models to detect theft, fraud, and organized crime within Amazon's global supply chain and operations. The scientist will own KPIs, automate detection, analyze operational defects, contribute to strategy, and integrate ML models into applications. The role requires a PhD or Master's with significant applied research experience in ML, particularly in fraud detection and organized crime.

This role focuses on developing and deploying machine learning and generative AI models for Amazon's Sponsored Products and Brands Ads, aiming to personalize advertising experiences and optimize the ad-serving process. It involves data analysis, model development, A/B testing, and research into new AI solutions, with a strong emphasis on productionizing these models for a large-scale consumer product.

Applied Scientist role at Amazon Advertising focusing on building full life-cycle machine learning solutions, including scaling ad performance insights through agentic systems/LLMs. The role involves hands-on analysis, modeling with large datasets, productionizing ML models with engineers, running A/B experiments, and researching innovative ML approaches to drive monetization and sales for Amazon's advertising products.

Software Development Engineer to develop a multi-modal, multi-lingual web-scale information retrieval system that combines LLMs with retrieval systems for question-answering, knowledge synthesis, and information grounding. The role involves building data pipelines, optimizing core algorithms and ranking models, designing data structures and indexing techniques, optimizing performance and scalability, integrating ML models, and developing control plane systems for the IR system infrastructure. Experience with Rust, large-scale distributed systems, and ML integration is required.

Software Development Engineer (Java/AWS) for Alexa AI team, focusing on building and delivering innovative consumer software products and industry-leading conversational assistant capabilities. The role involves owning large-scale customer-facing features, designing and architecting innovative consumer products, and contributing to the core runtime orchestration platform for conversational agents using LLMs and Gen AI.

Software Development Manager for Amazon Web Services (AWS) Elastic Block Store (EBS) Capacity Engineering team. The role involves leading a team to invent and build a new capacity forecasting and planning platform using AI/ML, including machine learning models, algorithms, and agents, to automate capacity planning and purchasing for AWS data centers globally. The focus is on optimizing fleet servers, predicting demand, and balancing service, infrastructure, financial, and business constraints to support the rapid growth of AWS, especially driven by AI workloads.

Software Development Engineer III role focused on building conversational ad experiences using generative AI and LLMs for Amazon Ads. The role involves designing and developing new features, integrating AI solutions, pioneering conversational AI approaches, and creating scalable, low-latency systems. It requires strong software engineering skills and collaboration with science teams to implement LLM models in production and contribute to LLMOps.

Software Development Engineer II at Amazon focused on building advanced robotics systems that integrate AI, control systems, and mechanical design for automation. The role involves developing a software framework for robot deployment, generalization, integration with large models and control techniques, and human-safe solutions. Key responsibilities include designing manipulation components, cloud/edge inferencing, real-time communication, and human-machine interfaces.

Software Development Engineer focused on real-time ad personalization and insights, leveraging generative AI technologies like latent diffusion models, LLMs, and RL to tailor visual ad experiences. The role involves investigating new technologies, building data pipelines, and developing platforms for ML model deployment in the Dynamic Creative Optimization (DCO) domain.

This Applied Scientist Intern role at Audible focuses on developing innovative AI solutions for recommendation, content understanding, and AI-powered product experiences. The intern will design and implement ML models, frameworks, and evaluation methodologies, enabling self-service automation and building AI systems that drive internal productivity and new product features. The role emphasizes practical implementation, collaboration with cross-functional teams, and raising AI fluency across the organization.

This role focuses on building AI-powered prototype solutions, agents, and automations to improve marketer productivity within Amazon Ads. The AI Builder will work with stakeholders to identify pain points, develop AI-native workflows, and ship AI use cases from pilot to production, establishing reusable frameworks and setting standards for AI transformation in marketing.

Data Scientist II role focused on developing and supporting data science methodologies and models for Amazon's global inventory management and buying programs. The role involves deriving causal inferences, modeling variations, and building tools to drive key decisions in buying and sourcing strategies, with a focus on massive datasets and operational efficiency.

Data Engineer role focused on building and operating data pipelines, models, and platform infrastructure for Ring's analytics, science, and AI initiatives. The role involves designing end-to-end data lifecycles and building multi-agent solutions to automate data engineering tasks. It also includes contributing to the shared data platform and AI/ML models.

Senior Software Development Engineer to lead architecture and design of AI data pipelines and large-scale distributed systems for Amazon Bedrock Knowledge Bases, a RAG service for enterprise data integration with FMs and agents. Focus on scaling services, innovating on Gen AI, and ensuring extensible, performant, and secure solutions.

This role focuses on designing, implementing, and managing AWS solutions for public sector clients, with a strong emphasis on Machine Learning and Generative AI. The consultant will act as a trusted advisor, guiding customers through their cloud journey and providing expertise on AWS services, ML best practices, and foundational model optimization. The role involves a mix of solution architecture, implementation, and knowledge sharing, with a requirement for Swedish National status and business-level Swedish proficiency.

Senior Software Development Engineer for Prime Video's Machine Learning Platform team, focusing on recommendation systems and ML infrastructure. The role involves end-to-end ownership of product, user experience, design, and technology to deliver state-of-the-art entertainment experiences. Responsibilities include designing and building ML infrastructure, tackling complex problems, delivering scalable ML platform solutions, writing code, mentoring engineers, and influencing technical strategy.

Senior Delivery Consultant for AWS Professional Services in Israel, focusing on designing and implementing end-to-end AI solutions for enterprise customers. This role involves architectural design, customer consulting, technical leadership, and developing generative AI applications like RAG systems and intelligent agents, with an emphasis on MLOps and responsible AI practices.

Software Development Engineer on the EC2 Nitro Machine Learning Systems team, focusing on building and optimizing performance measurement infrastructure for AI/ML workloads. The role involves establishing best-known configurations, translating performance insights into technical requirements, and analyzing training/inference performance across accelerated platforms. It requires expertise in low-level systems, ML frameworks, and serving layers.

Senior Software Development Engineer role focused on building and scaling EC2 compute platforms for machine learning workloads, including training and inference for LLMs and multimodal systems. The role involves designing innovative technologies, leading technical projects, developing regression testing systems, and collaborating with hardware teams to optimize platform designs for ML performance.

Applied Science Manager leading a team focused on optimizing drone fleet mission planning and orchestration using ML/RL and geospatial data to maximize deliveries per hour. The role involves path planning, scheduling, system architecture, and cross-functional collaboration, with a focus on turning research into deployed features.

Senior Software Development Engineer role focused on building and scaling machine learning infrastructure for EC2 Nitro, supporting training and inference workloads for various ML applications including LLMs and multimodal systems. The role involves designing innovative technologies, leading technical projects, developing regression testing systems, and collaborating with hardware teams to optimize platform designs for ML performance.

Senior Software Development Engineer to lead design and implementation of distributed AI data pipelines and large-scale distributed systems for Amazon Bedrock Knowledge Bases, a RAG service. Focus on technical leadership, architecture, and balancing execution with long-term needs.

This role focuses on applying ML and AI to solve business problems within Amazon's third-party marketplace, specifically concerning seller fees. The scientist will develop and deploy models for fee accuracy, policy automation, and seller explanations, working closely with engineering and product teams to productionize solutions. The role emphasizes rigorous experimentation, causal inference, and contributing to the scientific culture.

Software Development Engineer 2 role focused on building simulation environments, agent interaction servers, data pipelines, and managing agent fleets for AGI Data Services, supporting LLM and Gen-AI model development at Amazon.

Applied Scientist II role focused on enhancing ML accelerator software (Trainium/Inferentia) to accelerate customer adoption. Responsibilities include developing ML/RL for code generation/optimization, creating ML compiler techniques, building validation tools, and designing high-performance kernels. The role involves working with customers, engineering teams, and research communities to advance ML systems, with a focus on inference performance and training cost optimization.

Software Development Manager for Amazon Ads' Sponsored Products and Brands team, focusing on leveraging generative AI and ML to revolutionize advertising experiences. The role involves managing engineers, developing roadmaps for ML/GenAI solutions in ad retrieval, fostering cross-team collaboration, and hiring/developing talent. The team operates full-stack, from backend services to shopper-facing experiences, aiming to deliver personalized and context-aware advertising.

Software Development Engineer III role focused on leading the technical direction for autonomous navigation stack components, including task planning, motion planning, and control for production robotics systems. The role involves architecting and implementing ML/AI-based systems, impacting thousands of robots, and working with state-of-the-art technologies in a scaling environment.

This role focuses on applying machine learning to video processing, understanding, and content monetization within AWS Elemental. The scientist will prototype, optimize, and deploy advanced models into production, collaborate with cross-functional teams, and develop scalable solutions. The role also involves mentoring, contributing to operational tooling, and potentially publishing applied research. The primary focus is on shipping AI-powered features and enhancements for video services.

This role focuses on architecting and developing end-to-end solutions that leverage Generative AI technologies like Amazon Quick and Bedrock for customer success. It involves designing and implementing solution blueprints, integrating Gen AI capabilities into existing systems, and providing guidance on data preparation, model training, and deployment strategies. The role also includes customer updates and SFDC hygiene.

Software Development Engineer to build and improve a generative AI-powered shopping assistant (Rufus) for millions of Amazon customers, focusing on product research, comparisons, and recommendations.

Sr. Applied Scientist at Amazon Prime Video focused on building and deploying sophisticated machine learning systems for content presentation and discovery. The role involves developing multi-modal embeddings, contextualized ranking systems, and reinforcement learning frameworks, with a strong emphasis on bridging science and engineering for production-scale deployment. Responsibilities include leading cross-functional science initiatives, mentoring junior talent, and working with large-scale data to create personalized customer experiences.

PhD student internship focused on applying machine learning, NLP, computer vision, automated reasoning, or robotics to solve complex business problems and deploy solutions into production at Amazon.

Machine Learning Engineer role focused on designing, implementing, and scaling Generative AI solutions for enterprise customers on AWS. The role involves working directly with customers to understand their needs, select and fine-tune models, develop proof-of-concepts, and provide technical guidance throughout the project lifecycle. It emphasizes customer success and driving AI transformation.

This role focuses on building and deploying machine learning models to improve customer experience through personalized recommendations on Amazon's retail website. The scientist will leverage large-scale data and computing resources to understand customer behavior, develop state-of-the-art recommendation models, and measure their impact on the business.

The role focuses on building the AI/ML foundation for sustainability initiatives, involving data identification, ingestion, harmonization, storage, and maintenance of strategic models and data. It requires developing data pipelines, implementing data collection strategies, and creating monitoring and alarming systems for sustainability-related AI solutions.

This role focuses on building the AI/ML foundation for sustainability initiatives, involving data and AI/ML infrastructure implementation, data pipeline development, and establishing ML Ops best practices. The goal is to support various sustainability efforts like carbon footprinting and climate risk monitoring.

Applied Scientist role focused on building next-generation software verification tools by combining AI, cloud computing, and formal verification expertise. The role involves understanding customer needs, identifying tools and methods for verification, and exploring generative AI techniques for requirements formalization, test generation, and proof repair.

This role focuses on architecting and implementing AI solutions, specifically agentic AI, within Amazon Connect for enterprise customers. It involves guiding customers on model selection, prompt configuration, tool integration, and ensuring customer data is ready for AI agents. The role is hands-on, requiring coding, integration building, and collaboration with customer teams to move solutions from proof-of-concept to production.

This role focuses on inventing and implementing scientific solutions, including machine learning models, for EC2 capacity intelligence. It involves developing and productionizing data-driven solutions for efficient capacity and demand allocation, aiming to improve customer experience. The role requires expertise in ML/DL and applying scientific principles to real-world business challenges, with a strong emphasis on bridging science and engineering.